Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Machine Learning-Driven Risk Assessment in Cyber Threat Intelligence: Automating Vulnerability Detection

Dr. Sarah Patel
Senior Lecturer, Department of Information Technology, University of Toronto, Toronto, Canada
Cover

Published 28-09-2024

Keywords

  • machine learning,
  • cyber threat intelligence,
  • vulnerability detection,
  • risk assessment,
  • supervised learning,
  • unsupervised learning
  • ...More
    Less

How to Cite

[1]
Dr. Sarah Patel, “Machine Learning-Driven Risk Assessment in Cyber Threat Intelligence: Automating Vulnerability Detection”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 107–114, Sep. 2024, Accessed: Nov. 14, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/177

Abstract

Cyber threat intelligence (CTI) plays a crucial role in mitigating risks and preventing vulnerabilities in network infrastructures. However, the increasing complexity of cyber-attacks has outpaced the capabilities of traditional threat intelligence systems, necessitating more advanced and automated solutions. This paper explores the integration of machine learning (ML) algorithms into CTI for enhancing risk assessment and vulnerability detection. Machine learning techniques such as supervised, unsupervised, and reinforcement learning are examined for their efficacy in automating threat detection, predicting vulnerabilities, and improving decision-making processes in real-time environments. The study also analyzes the strengths and limitations of different ML models, focusing on accuracy, detection speed, and adaptability to new threats. By leveraging data-driven approaches, ML algorithms can significantly reduce human intervention, allowing for faster response times and more accurate assessments of potential risks. This research concludes by discussing the future implications of ML in cyber threat intelligence and the ongoing challenges related to data quality, interpretability, and system scalability.

Downloads

Download data is not yet available.

References

  1. Vangoor, Vinay Kumar Reddy, et al. "Zero Trust Architecture: Implementing Microsegmentation in Enterprise Networks." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 512-538.
  2. Gayam, Swaroop Reddy. "Artificial Intelligence in E-Commerce: Advanced Techniques for Personalized Recommendations, Customer Segmentation, and Dynamic Pricing." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 105-150.
  3. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Predictive Maintenance of Banking IT Infrastructure: Advanced Techniques, Applications, and Real-World Case Studies." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 86-122.
  4. Putha, Sudharshan. "AI-Driven Predictive Analytics for Maintenance and Reliability Engineering in Manufacturing." Journal of AI in Healthcare and Medicine 2.1 (2022): 383-417.
  5. Sahu, Mohit Kumar. "Machine Learning for Personalized Marketing and Customer Engagement in Retail: Techniques, Models, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 219-254.
  6. Kasaraneni, Bhavani Prasad. "AI-Driven Policy Administration in Life Insurance: Enhancing Efficiency, Accuracy, and Customer Experience." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 407-458.
  7. Kondapaka, Krishna Kanth. "AI-Driven Demand Sensing and Response Strategies in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 459-487.
  8. Kasaraneni, Ramana Kumar. "AI-Enhanced Process Optimization in Manufacturing: Leveraging Data Analytics for Continuous Improvement." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 488-530.
  9. Pattyam, Sandeep Pushyamitra. "AI-Enhanced Natural Language Processing: Techniques for Automated Text Analysis, Sentiment Detection, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 371-406.
  10. Kuna, Siva Sarana. "The Role of Natural Language Processing in Enhancing Insurance Document Processing." Journal of Bioinformatics and Artificial Intelligence 3.1 (2023): 289-335.
  11. George, Jabin Geevarghese, et al. "AI-Driven Sentiment Analysis for Enhanced Predictive Maintenance and Customer Insights in Enterprise Systems." Nanotechnology Perceptions (2024): 1018-1034.
  12. P. Katari, V. Rama Raju Alluri, A. K. P. Venkata, L. Gudala, and S. Ganesh Reddy, “Quantum-Resistant Cryptography: Practical Implementations for Post-Quantum Security”, Asian J. Multi. Res. Rev., vol. 1, no. 2, pp. 283–307, Dec. 2020
  13. Karunakaran, Arun Rasika. "Maximizing Efficiency: Leveraging AI for Macro Space Optimization in Various Grocery Retail Formats." Journal of AI-Assisted Scientific Discovery 2.2 (2022): 151-188.
  14. Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "Relocation of Manufacturing Lines-A Structured Approach for Success." International Journal of Science and Research (IJSR) 13.6 (2024): 1176-1181.
  15. Paul, Debasish, Gunaseelan Namperumal, and Yeswanth Surampudi. "Optimizing LLM Training for Financial Services: Best Practices for Model Accuracy, Risk Management, and Compliance in AI-Powered Financial Applications." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 550-588.
  16. Namperumal, Gunaseelan, Akila Selvaraj, and Yeswanth Surampudi. "Synthetic Data Generation for Credit Scoring Models: Leveraging AI and Machine Learning to Improve Predictive Accuracy and Reduce Bias in Financial Services." Journal of Artificial Intelligence Research 2.1 (2022): 168-204.
  17. Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Yeswanth Surampudi. "Enhancing Algorithmic Trading Strategies with Synthetic Market Data: AI/ML Approaches for Simulating High-Frequency Trading Environments." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 333-373.
  18. Pradeep Manivannan, Amsa Selvaraj, and Jim Todd Sunder Singh. “Strategic Development of Innovative MarTech Roadmaps for Enhanced System Capabilities and Dependency Reduction”. Journal of Science & Technology, vol. 3, no. 3, May 2022, pp. 243-85
  19. Yellepeddi, Sai Manoj, et al. "Federated Learning for Collaborative Threat Intelligence Sharing: A Practical Approach." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 146-167.